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from typing import Union, Optional
from pathlib import Path
import torch
import torch.nn as nn
from safetensors import safe_open
from safetensors.torch import save_file
from transformers import CLIPTextModel
from transformers.models.clip import CLIPTextConfig
from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
from models.sparse import PseudoSparseEmbedding
def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: Optional[float] = None) -> nn.Embedding:
old_num_embeddings, old_embedding_dim = old_embedding.weight.shape
if old_num_embeddings == new_num_embeddings:
return old_embedding
n = min(old_num_embeddings, new_num_embeddings)
new_embedding = nn.Embedding(
new_num_embeddings,
old_embedding_dim,
device=old_embedding.weight.device,
dtype=old_embedding.weight.dtype
)
if initializer_factor is not None:
new_embedding.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
else:
nn.init.zeros_(new_embedding.weight.data)
new_embedding.weight.data[:n, :] = old_embedding.weight.data[:n, :]
return new_embedding
class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, dropout_p: float = 0.0):
super().__init__(config)
self.token_embedding = embeddings.token_embedding
self.position_embedding = embeddings.position_embedding
self.initializer_factor = config.initializer_factor
self.token_override_embedding = PseudoSparseEmbedding(
self.token_embedding.embedding_dim,
dropout_p=dropout_p,
device=self.token_embedding.weight.device,
dtype=self.token_embedding.weight.dtype,
)
def resize(self, size: int):
self.token_override_embedding.resize(size)
self.token_embedding = resize_embedding(self.token_embedding, size, self.initializer_factor)
def add_embed(
self,
token_ids: Union[int, list[int]],
initializer: Optional[Union[int, list[int], torch.FloatTensor]] = None,
initializer_noise: float = 0.0,
):
if isinstance(token_ids, int):
token_ids = [token_ids]
if initializer is None:
initializer = token_ids
if isinstance(initializer, int):
initializer = [initializer]
if isinstance(initializer, list):
initializer = (initializer * len(token_ids))[:len(token_ids)]
with torch.no_grad():
initializer = self.get_embed(initializer)
initializer = initializer.to(
device=self.token_embedding.weight.device,
dtype=self.token_embedding.weight.dtype,
)
if initializer_noise != 0:
initializer += torch.randn_like(initializer) * initializer_noise
token_ids = torch.tensor(token_ids, dtype=torch.long)
self.token_embedding.weight.data[token_ids] = initializer
self.token_override_embedding.set(token_ids, initializer)
def load_embed(self, input_ids: list[int], filename: Path):
with safe_open(filename, framework="pt", device="cpu") as file:
self.add_embed(input_ids, file.get_tensor("embed"))
def save_embed(self, input_ids: list[int], filename: Path):
save_file({"embed": self.get_embed(input_ids)}, filename)
def persist(self):
input_ids = torch.arange(
self.token_embedding.num_embeddings,
device=self.token_override_embedding.mapping.device
)
embs, mask = self.token_override_embedding(input_ids)
if embs is not None:
input_ids = input_ids[mask]
self.token_embedding.weight.data[input_ids] = embs
self.token_override_embedding.unset(input_ids)
def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
if isinstance(input_ids, list):
input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long)
embs = self.token_embedding(input_ids)
embs_override, mask = self.token_override_embedding(input_ids)
if embs_override is not None:
embs[mask] = embs_override
return embs
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.get_embed(input_ids)
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
def patch_managed_embeddings(text_encoder: CLIPTextModel, dropout_p: float = 0.0) -> ManagedCLIPTextEmbeddings:
text_embeddings = ManagedCLIPTextEmbeddings(text_encoder.config, text_encoder.text_model.embeddings, dropout_p)
text_encoder.text_model.embeddings = text_embeddings
return text_embeddings
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